The classification tree (CT) may be used to establish explicit classification rules for Satellite Imagery (SI). However, the accuracy of explicit classification rules attained by this method is poor. Back-propagation networks (BPN) and the support vector machine (SVM) may both be used to establish highly accurate models for predicting the classification of SI. However, neither is able to generate explicit

Shared-memory graph processing is usually more efficient than in a cluster in terms of cost effectiveness, ease of programming and runtime. However, the limited memory capacity of a single machine and the huge sizes of graphs restrains its applicability. Hence, it is imperative to reduce memory footprint. We observe that index compression holds promise and propose CIC-PIM, a lightweight encoding with

Rendezvous algorithms encode a communication pattern that is useful when processors sending data don’t know who the receiving processors should be, or vice versa. The idea is to define an intermediate decomposition where datums from different sending processors can ”rendezvous” to perform a computation, in a manner that both the senders and eventual receivers of the results can identify the appropriate

Reliability evaluation of interconnection networks is of significant importance to the design and maintenance of interconnection networks. The extra edge-connectivity and component edge-connectivity are two important parameters for the reliability evaluation of interconnection networks. In this paper, we determine the h-extra edge-connectivity of an n-dimensional augmented cube for h≤2[n2], n≥2 and

Cloud computing provides scalable network services and makes network management more flexible by combining Software-Defined Networking (SDN). Through the northbound interface (e.g., REST API) offered by the SDN controller, users can easily deploy diversified applications to access the network resources. However, exploiting the openness of the northbound interface, malicious applications abuse APIs

The upcoming digitalization in the context of Cyber-physical Systems (CPS), enabled through Internet-of-Things (IoT) infrastructures, require efficient methods for distributed processing of the data, that is generated by multiple sources. We address the problem of obstacle detection and localization through data clustering, which is a common component for data processing in the fusion of multiple point

The constructions of node-disjoint paths have been well applied to the study of connectivity, diameter, parallel routing, reliability, and fault tolerance of an interconnection network. In order to minimize the transmission cost and latency, the total length and maximal length of the node-disjoint paths should be minimized, respectively. The construction of node-disjoint paths with their maximal length

Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. When applying deep GNNs on large graphs, it is still challenging to perform training in an efficient and scalable way. We propose a novel parallel training framework. Through sampling small subgraphs as minibatches, we reduce training workload by orders of magnitude compared with state-of-the-art minibatch

This paper presents a novel leaderless protocol (FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures) with a low communicational complexity and which allows a set of nodes to come to a consensus on a value of a single bit. The paper makes the assumption that part of the nodes are Byzantine, and are thus controlled by an adversary who intends to either delay the consensus, or break

We design distributed algorithms to compute approximate solutions for several related graph optimization problems. All our algorithms have round complexity being logarithmic in the number of nodes of the underlying graph and in particular independent of the graph diameter. By using a primal–dual approach, we develop a 2(1+ϵ)-approximation algorithm for computing the coreness values of the nodes in

A fast parallel graph partitioner can benefit many applications by reducing data transfers. The online methods for partitioning graphs have to be fast and they often rely on simple one-pass streaming algorithms, while the offline methods for partitioning graphs contain more involved algorithms and the most successful methods in this category belong to the multilevel approaches. In this work, we assess

The interconnection network is a key element in High-Performance Computing (HPC) and Datacenter (DC) systems whose performance depends on several design parameters, such as the topology, the switch architecture, and the routing algorithm. Among the most common topologies in HPC systems, the Fat-Tree offers several shortest-path routes between any pair of end-nodes, which allows multi-path routing schemes

With the advent of the Industry 4.0 era, the development of smart cities based on the Internet of Things (IoT) has reached a new level. As a key component of the Internet of Things (IoT), the security of wireless sensor networks (WSN) has received widespread attention. Among them, Energy Internet, as an important part to support the construction of smart cities, its security and reliability research

Machine Learning (ML) has become a critical tool enabling new methods of analysis and driving deeper understanding of phenomena across scientific disciplines. There is a growing need for “learning systems” to support various phases in the ML lifecycle. While others have focused on supporting model development, training, and inference, few have focused on the unique challenges inherent in science, such

The Dragonfly topology is currently one of the most popular network topologies in high-performance parallel systems. The interconnection networks of many of these systems are built from components based on the InfiniBand specification. However, due to some constraints in this specification, the available versions of the InfiniBand network controller (OpenSM) do not include routing engines based on

Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants’ activity and to collect insightful reports from the environment (e.g., air quality, network quality). However, most of existing crowdsourced datasets systematically tag data

Service migration in mobile edge computing is a promising approach to improving the quality of service (QoS) for mobile users and reducing the network operational cost for service providers as well. However, these benefits are not free, coming at costs of bulk-data transfer, and likely service disruption, which could consequently increase the overall service costs. To gain the benefits of service migration

In this study, we develop a novel data reconstruction technique for parallel storage systems housed in modern data centers. We advocate for erasure-coded data storage systems to archive warm data (a.k.a., unpopular data), which attract a limited number of accesses or updates. Different from hot or cold data, warm data have to be treated in a distinctive way to optimize system performance and storage-space

The NP-hard problem of task scheduling with communication delays (P|prec,cij|Cmax) is often tackled using approximate methods, but guarantees on the quality of these heuristic solutions are hard to come by. Optimal schedules are therefore invaluable for properly evaluating these heuristics, as well as being very useful for applications in time critical systems. Optimal solving using branch-and-bound

The technology-driven shift in distributed network systems, Internet technology and energy grid digitization have changed the traditional spoke-hub distribution models to the more flexible and giant network-to-networks paradigm known as Energy Cloud. This energy cloud paradigm has the ability to adapt to the large-scale distributed energy resources with dynamic demand-response associated with smart

Large-scale data-intensive applications provide services to users by routing service requests to geographically distributed data centers interconnected by Internet links. In order to achieve good reliability and data access latency performance, cloud service providers often simultaneously place multiple copies of the data in different data centers. The network communication required for updating the

Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning

Fog computing is an emerging paradigm that brings computing capabilities closer to distributed IoT devices, which provides networking services between end devices and traditional cloud data centers. One important mission is to further reduce the monetary cost of fog resources while meeting the ever-growing demands of multiple users. In this paper, we focus on minimizing the total cost for multiple

Self-stabilizing and silent distributed algorithms for token distribution in rooted tree networks are given. Initially, each process of a graph holds at most ℓ tokens. Our goal is to distribute the tokens uniformly in the whole network so that every process holds exactly k tokens. In the initial configuration, the total number of tokens in the network may not be nk where n is the number of processes

We study leader election in unidirectional rings of homonyms that have no a priori knowledge of the number of processes. In this context, we show that there exists no algorithm that solves the process-terminating leader election problem for the class of asymmetrically labeled unidirectional rings. More precisely, we prove that there is no process-terminating leader election algorithm even for the subclass

Elephant flows (elephants) refer to the sequences of packets that contribute only 10% of the total volume but consume over 90% of the network bandwidth. They often cause network congestion and should be efficiently managed. Present cloud data centers often involve host- and switch-based approaches to detect and schedule elephants, but suffer (1) each host and switch in the network needs to be customized

3D tomographic imaging requires the computation of solutions to very large inverse problems. In many applications, iterative algorithms provide superior results, however, memory limits in available computing hardware restrict the size of problems that can be solved. For this reason, iterative methods are not normally used to reconstruct typical data sets acquired with lab based CT systems. We thus

Emerging scientific workflows running at extreme scale are composed of multiple applications that interact and exchange data at runtime. While staging-based approaches, e.g. in-situ/in-transit processing, are promising, dynamic behaviors (e.g. data volumes and distributions) in coupled applications and varying resource constraints at runtime make the efficient use of these techniques challenging. Addressing

Early-stage disease risk prediction can be beneficial to improve the health of the mass and can reduce the economic burden of late treatment. Machine learning has played a pivotal role in predictive systems, which requires achieving a specific degree of accuracy for healthcare systems. Most recently researchers have found the necessity of bridging between epidemiology and machine learning classifications

With the popularity of the Internet of Things (IoT), data integrity verification in the edge cloud storage attracts attentions from many researchers. Due to the over dependence of the Third Party Auditor (TPA) and the dynamical nature of the IoT data, the traditional data integrity verification framework for cloud storage can hardly work. To satisfy the characteristics of the IoT and avoid the over

In this paper, a new distributed multi-agent framework based on the three layers’ fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics

Stochastic gradient descent (SGD) is a popular stochastic optimization method in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel SGD (Zinkevich, 2010), often require all nodes to have the same performance or to consume equal quantities of data. However, these requirements are difficult to satisfy when the parallel SGD algorithms run in a heterogeneous computing environment;

Machine learning and artificial intelligence (AI) applications often rely on performing many small matrix operations—in particular general matrix–matrix multiplication (GEMM). These operations are usually performed in a reduced precision, such as the 16-bit floating-point format (i.e., half precision or FP16). The GEMM operation is also very important for dense linear algebra algorithms, and half-precision

Herlihy showed that multiprocessors must support advanced atomic objects, such as compare-and-swap, to be able to solve any arbitrary synchronization task among any number of processes (Herlihy, 1991). Elementary objects such as read-write registers and fetch-and-add are fundamentally limited to at most two processes with respect to solving an arbitrary synchronization task. Later, it was also shown

Smart Cities evolve into complex and pervasive urban environments with a citizens’ mandate to meet sustainable development goals. Repositioning democratic values of citizens’ choices in these complex ecosystems has turned out to be imperative in an era of social media filter bubbles, fake news and opportunities for manipulating electoral results with such means. This paper introduces a new paradigm

Multi/Many-core systems based on the traditional electrical network-on-chip are confronted with the limited bandwidth, high latency, and reliability challenges. The emerging optical solution of silicon photonics has promised to improve the design parameters of electrical interconnections for future multiprocessor system on chips. Although the optical network-on-chip has advantages in terms of reliability

The technological evolution of the smart grids is going to take its shape in the form of a new paradigm called the Internet of Energy (IoE); which is considered to be the convergence of internet, communication, and energy. Like other evolved technologies, the IoE inherits security vulnerabilities from its constituents that need to be addressed. Intrusion Detection Systems (IDS) have been used to counteract

Page faults occurring within transactions jeopardize concurrency in Intel Restricted Transactional Memory (RTM). To make progress in spite of page-fault-induced aborts, the program must resort to the non-speculative fallback path and re-execute the affected transaction. Since the atomicity of a non-speculative transaction is guaranteed by impeding the execution of any other speculative transactions

Caches are an important component of modern computing systems given their significant impact on performance. In particular, caches play a key role in the cloud due to the nature of large-scale, data-intensive processing. One of the key challenges for the cloud providers is how to share the caching capacity among tenants, under the circumstance that each often requires a different degree of quality

Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. However, anomaly detection models must be trained for a long time

Blockchain has received a widespread attention because of its decentralized, tamper-proof, and transparent nature. Blockchain works over the principle of distributed, secured, and shared ledger, which is used to record, and track data within a decentralized network. This technology has successfully replaced certain systems of economic transactions in organizations and has the potential to overtake

Energy cloud systems continue to shape the future of the energy sector. The complexity of energy cloud systems stems from their widespread and distributed aspects such as renewable energy sources, energy storage, customers engagement, social media and the advancements in communication and computing technologies. The unprecedented large-scale growth of energy cloud systems requires a crucial and dramatic

The K-means algorithm is one of the most popular algorithms in Data Science, and it is aimed to discover similarities among the elements belonging to large datasets, partitioning them in K distinct groups called clusters. The main weakness of this technique is that, in real problems, it is often impossible to define the value of K as input data. Furthermore, the large amount of data used for useful

Clustering, which explores the visualization and distribution of data, has recently been studied widely. Although the existing clustering algorithms can well detect arbitrary shape clusters, most of them face the limitation that they cluster points on the basis of two physical metrics, distance and density, but ignore the orientation relationship of data distribution. Beside, they have a difficulty

Smart city projects stand out for including a wide array of communication systems and heterogeneous technologies operated by multiple entities that collect data from citizens in very different ways. Basically, these projects involve three main actors that may have conflicting interests regarding the ownership and exploitation of the generated data: public administrations, service providers and citizens

The blockchain whitepaper contains detailed technical and business information, so its analysis is important for blockchain text mining. Previous works focus on analyze homogeneous objects and relations. The main problem, however, is these works do not take into account the heterogeneity of information. This paper presents a new methodology for whitepapers analysis by designing heterogeneous graph

In this paper, we focus on the design of optimal relay and jammer selection strategy in relay-aided wireless networks. Different from previous works, assuming that the channel state information (CSI) of illegitimate nodes was available and only an eavesdropper existed, we first analyze disadvantages of joint relay and jammer selection (JRJS), average optimal relay selection (AORS), traditional maximum

With the increasing viability of Internet of Things (IoT), more devices are expected to be connected in a smart city environment. It can provide many benefits for people’s daily life, but is also susceptible to many security threats in practice. Intrusion detection systems (IDSs), especially signature-based IDSs, are one of the most commonly adopted security mechanisms to safeguard various network

Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-core system while adjusting resources to expend a minimum of energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core

We present a self-stabilizing leader election algorithm for general networks, with space-complexity O(logΔ+loglogn) bits per node in n-node networks with maximum degree Δ. This space complexity is sub-logarithmic in n as long as Δ=no(1). The best space-complexity known so far for general networks was O(logn) bits per node, and algorithms with sub-logarithmic space-complexities were known for the ring

Understanding memory performance in multi-core platforms is a prerequisite to perform optimizations. To this end, this paper presents analytical models based on Stochastic Reward Nets (SRNs) to model and evaluate the memory performance of Non-Uniform Memory Access (NUMA) multi-core architectures. The approach considers the details of the architecture and first proposes a monolithic SRN model that evaluates

The Time-Sensitive Networking (TSN) set of standards introduces in IEEE 802.1 switches and end stations novel features to meet the requirements of a broad spectrum of applications that are characterized by time-sensitive and mission-critical traffic flows. In particular, the IEEE802.1Qbv-2015 amendment introduces enhancements that provide temporal isolation for scheduled traffic, i.e., a traffic class

Parallel computations with irregular memory access patterns are often limited by the memory subsystems of multi-core CPUs, though it can be difficult to pinpoint and quantify performance bottlenecks precisely. We present a method for estimating volumes of data traffic caused by irregular, parallel computations on multi-core CPUs with memory hierarchies containing both private and shared caches. Further

Smart surface is a new conveying technology composed of a 2D planar surface presenting a matrix of distributed autonomous blocks. Every block contains a micro-electro-mechanical system (MEMS) actuator that controls the transfer of a possible object located above the block to the neighboring blocks, using air-jet forces. The spatial characteristics of the blocks impose some limits on the memory, energy

The alternating direction method of multipliers (ADMM) is a powerful operator splitting technique for solving structured convex optimization problems. Due to its relatively low per-iteration computational cost and ability to exploit sparsity in the problem data, it is particularly suitable for large-scale optimization. However, the method may still take prohibitively long to compute solutions to very

Industry 4.0 technological expansion and the multiple accesses to the diverse Smart Grid domains (power networks, control systems, market, customer premises) entail the need to provide efficient interconnection mechanisms with connection from anywhere, at any time and in anyhow. However, this type of requirement should not only consist in imposing interoperability solutions between entities and domains

Identifying long pairwise maximal common substrings among a large set of sequences is a frequently used construct in computational biology, with applications in DNA sequence clustering and assembly. Due to errors made by sequencers, algorithms that can accommodate a small number of differences are of particular interest. Formally, let D be a collection of n sequences of total length N, ϕ be a length

Online Social Networks (OSNs) represent today a big communication channel where users spend a lot of time to share personal data. Unfortunately, the big popularity of OSNs can be compared with their big privacy issues. Indeed, several recent scandals have demonstrated their vulnerability. Decentralized Online Social Networks (DOSNs) have been proposed as an alternative solution to the current centralized

We analyze time and energy performance of distributed computations in heterogeneous systems with hierarchical memory. Different levels of memory hierarchy have different time and energy efficiency. Core memory may be too small to hold whole load to be processed, while computations using external storage are expensive in time and energy. In order to avoid the costs of processing the load in the external

The focus of extracting colossal closed itemsets from high dimensional biological datasets has been great in recent times. A massive set of short and average sized mined itemsets do not confine complete and valuable information for decision making. But, the traditional itemset mining algorithms expend a gigantic measure of time in mining a massive set of short and average sized itemsets. The greater